Speaker-Aware Anti-Spoofing
Abstract
We address speaker-aware anti-spoofing, where prior knowledge of the target speaker is incorporated into a voice spoofing countermeasure (CM). In contrast to the frequently used speaker-independent solutions, we train the CM in a speaker-conditioned way. As a proof of concept, we consider speaker-aware extension to the state-of-the-art AASIST (audio anti-spoofing using integrated spectro-temporal graph attention networks) model. To this end, we consider two alternative strategies to incorporate target speaker information at the frame and utterance levels, respectively. The experimental results on a custom protocol based on ASVspoof 2019 dataset indicates the efficiency of the speaker information via enrollment: we obtain maximum relative improvements of 25.1% and 11.6% in equal error rate (EER) and minimum tandem detection cost function (t-DCF) over a speaker-independent baseline, respectively.
Cite
@article{arxiv.2303.01126,
title = {Speaker-Aware Anti-Spoofing},
author = {Xuechen Liu and Md Sahidullah and Kong Aik Lee and Tomi Kinnunen},
journal= {arXiv preprint arXiv:2303.01126},
year = {2023}
}